Driver inattention is a frequent cause of automobile crashes and fatalities. This issue has received considerable attention from the scientific community in recent years. Methods of detecting episodes of driver inattention in real-time hold promise for alleviating the human and economic costs of this safety critical issue. Drivers can be inattentive for a variety of reasons, the most obvious being distraction from mobile devices (Caird et al., 2008) or other external factors. However, many distracted driving crashes occur in the absence of an obvious visual or manual distraction. Mind wandering has been suggested as a potential source of many of these distracted driving crashes.

For most people, driving is a highly-overlearned task. Consequently, many of the tasks of everyday driving—lane and speed maintenance, stopping at signaled intersections, etc.—tend to occur relatively automatically. In addition, many trips are routinized with drivers taking the same routes back and forth to work, the grocery store, or other frequently visited locations, which further promotes automaticity, allowing attention to be devoted to other activities. The routine nature of the driving task, particularly along familiar or monotonous routes, creates an environment ripe for internal distraction or mind wandering, as we will refer to it here. Nevertheless, to maintain safety, drivers must remain attentive to a wide variety of stimuli that may represent latent hazards (Fisher et al., 2002) and be able to swiftly and accurately respond to unexpected events.

There are many varieties of attention (Parasuraman, 1998), but a well-accepted theoretical distinction is between external and internal attention (Chun et al., 2011). External (or “bottom-up”) attention is triggered reflexively by environmental events. Internal (or “top-down”) attention is voluntary or involuntary application of cognitive resources away from the external environment towards internal thoughts. Most empirical research on internal attention has investigated the pursuit of goals relevant to events in the environment—e.g., such as searching for a landmark while driving in an unfamiliar neighborhood. But, by definition, internal attention can also be devoted to thoughts and memories quite unrelated to any external event. Much less research has been devoted to this topic (Forster and Lavie, 2014), but in recent years a small but growing body of work has examined an aspect of such internal attention—mind wandering (Giambra, 1995; Smallwood and Schooler, 2006).

It is well documented that driving performance is modulated by factors such as effort, fatigue and time on task (Robertson et al., 1997; Grier et al., 2003; Helton and Warm, 2008; May and Baldwin, 2009; Langner et al., 2010; Baldwin et al., 2014). Furthermore, several studies have shown that external distractions, such as talking or texting on a mobile phone, impair driving performance (Strayer et al., 2003; Strayer and Drews, 2007; Caird et al., 2008). Less appreciated and understood is the threat to safety posed by mind wandering behind the wheel, though mind wandering has been associated with an increased risk of being responsible for an automobile crash (Galéra et al., 2012). Fatigue associated with increased time on task may exacerbate both the prevalence and potential risk associated with mind wandering as it is associated with withdrawal of attention away from the driving task and can be considered a form of internal distraction (Williamson, 2007).

Mind wandering has been defined as, “a shift of attention away from a primary task toward internal information” (Smallwood and Schooler, 2006, p. 946). It often occurs without intention and may even occur without explicit awareness, making it a particular challenge to observe and measure. People may continue to move their eyes across a page of text (or the forward field of view while driving) without overtly attending to the viewed stimuli (Smallwood, 2011). Mind wandering is associated with increased activity in the default mode network (DMN) and decreased activity in the dorsal attention network (DAN). The DAN is integrally involved in controlling eye movements and directing exogenous attention to salient stimuli through top-down goal directed processing (Carretié et al., 2013). The DMN is sensitive to the presence of biologically salient non-task relevant stimuli, but this sensitivity is generally thought to come at the cost of processing task-relevant stimuli (Smallwood, 2011; Carretié et al., 2013). Smallwood (2011) has referred to this interplay between the DAN and DMN as a decoupling process, meaning that as attention is shifted toward one system it is withdrawn from the other. This decoupling may have important implications for driving by suggesting that the more drivers are engaged in mind wandering (activation of the DMN) the less likely they are to process external perceptual cues (potential hazards), particularly if the perceptual cues involve non-biologically salient stimuli (e.g., artificial sounds such as alarms and brake lights).

Two methods have typically been used to detect and quantify mind wandering: self-caught detection and probe-caught detection. The self-caught method of detection involves participants reporting when they notice their mind wandering. In contrast to self-caught methods of detection, probe techniques allow for sampling participants cognitive states throughout a task under experimenter control, thus allowing for the detection of mind wandering episodes that are uncaught by the participants themselves. Three types of probe techniques have been previously utilized. The first and most commonly used is intermittent presentation of questions such as, “Just now, were you mind wandering?” throughout an otherwise continuous task (e.g., Broadway et al., 2015). Another variation of this technique uses probe tones, prompting participants to indicate via a button press whether they were or were not mind wandering (Smallwood and Schooler, 2006). More recently, Seli et al. (2016) used a combination of these probe techniques to determine whether performance differs when participants were aware or unaware that they were mind wandering. Participants were asked to indicate whether their thoughts were on task or they were mind wandering just before they heard the probe tone. Additionally, if participants indicated that they were mind wandering, they were then asked to indicate if they were aware or unaware that they were mind wandering prior to the probe tone. The third probe-caught method involves participants indicating the content of their thoughts at the time of probe, leaving the experimenter to classify whether their thoughts constituted mind wandering (Smallwood and Schooler, 2006). However, by necessity, both self-caught and probe-caught methods likely disrupt participants primary task performance. This may be especially true when probes require more fine-grained judgments of the degree of mind wandering. An ideal mind wandering detection methodology would not require any response on the part of the participant, but rather would provide an on-line classification using some form of machine learning. However, currently such a technique awaits further exploration of the sensitivity and robustness of various metrics using some type of self-report technique.

Results of several recent studies utilizing measures derived from electroencephalography (EEG) substantiate this claim by observed reductions in perceptual sensitivity during periods of mind wandering (Smallwood et al., 2008; Braboszcz and Delorme, 2011). In a breath counting task, Braboszcz and Delorme (2011) report that in the 10 s prior to participants’ self-detecting a state of mind wandering, there was a significant reduction of alpha band activity combined with a diffuse increase in theta band activity, relative to the 10 s period following the button press, during which participant thought had presumably returned to the breath counting task. Modulation of theta band activity may provide a means of distinguishing mind wandering from other types of internal distractions. Savage et al. (2013) found that when participants were given a riddle to solve while performing a simulated driving task there was a similar increase in theta band as that reported by a study measuring frontal sites during mind wandering (Braboszcz and Delorme, 2011). However, in contrast to mind wandering, being internally distracted by a secondary cognitive task leads to a decrease in theta band activity over occipital electrode locations.

Oscillatory activity in the alpha band of the EEG is suggested to be related to attention processes (Klimesch, 2012), specifically the degree to which attention is allocated internally vs. externally. For example, greater alpha power at parietal and/or occipital electrode locations is associated with the failure to detect (Ergenoglu et al., 2004) or discriminate (Van Dijk et al., 2008; Roberts et al., 2014) visual stimuli, while spatial attention processes similarly modulate alpha power such that alpha is suppressed contralaterally and enhanced ipsilaterally to the attended location (Worden et al., 2000; Thut et al., 2006). In contrast, alpha power has been reported to be elevated for tasks in which attention is directly internally, such as working memory retention (Jensen et al., 2002). As periods of high alpha power are associated with lapses of attention to external stimuli (O’Connell et al., 2009; Zauner et al., 2012; Borghini et al., 2014) alpha power is expected to be greater during periods of mind wandering relative to periods of on task behavior.

In terms of event related potentials (ERPs), modulations of early perceptual and attentional components during mind wandering have been observed. Broadway et al. (2015) report that in a reading task the visual N1 component, thought to index orienting and enhancement of perceptual information (Hopfinger et al., 2004), was attenuated during mind wandering. The authors also report an attenuation of the P1 component, thought to index the inhibition of irrelevant information (Hopfinger et al., 2004), over the right hemisphere when participants reported mind wandering. Attenuation of the visual P1 was reported by Baird et al. (2014) bilaterally. However, it should be noted that such early components are relatively small and short-lasting and thus may be difficult to utilize in terms of on-line detection of mind wandering, especially.

More promising as a means of future on-line detection of mind wandering is the modulation of longer latency components such as the P300, a large, long-lasting ERP component thought to reflect allocation of attentional resources (Nieuwenhuis et al., 2005). Smallwood et al. (2008) found that the P300 evoked by visual stimuli in the sustained attention to response task (SART) was significantly attenuated during periods of self-reported mind wandering relative to periods of self-reported on-task behavior. Similar P300 attenuation during mind wandering was reported by Kam et al. (2014) in response to evaluation of more complex visual stimuli. The relatively high amplitude of the P300 relative to the background EEG allows reasonable extraction of this waveform from single trials for brain-computer interfaces, and has been an area of significant research during the past decade (Cecotti and Rivet, 2014).

Driver behavioral metrics such as speed variability, lane deviation, standard deviation of lateral position (SDLP) and steering reversal rate (SRR), have been shown to serve as an additional method to detect mind wandering (He et al., 2011, 2014; Bencich et al., 2014; Yanko and Spalek, 2014). However, previous research using these behavioral metrics have found inconsistent results that vary depending on the mind wandering detection method. For example, He et al. (2011) used the self-caught detection method and report that speed variability was decreased during mind wandering relative to on-task states. When using the probe detection method to detect instances of mind wandering, Yanko and Spalek (2014) found that mean speed was greater during mind wandering than on-task states. Conversely, using this same technique, Bencich et al. (2014) found that mean speed and speed variability were reduced during mind wandering compared to a state of alertness.

Further, He et al. (2014) found that under low cognitive load, which is thought to be similar to a mind wandering state, SDLP and SRR were reduced compared to a high cognitive load condition. He et al. (2014) interpreted these findings as suggesting that under high cognitive load, more effort and attention is needed to maintain lateral control, which could reflect an alert state. For example, during an attentive state (relative to mind wandering) participants drove at significantly greater speeds, had greater speed variability, greater SDLP and increased SRR (He et al., 2011; Bencich et al., 2014; Yanko and Spalek, 2014). However, though it is well-understood that mind wandering is associated with decreased attention to a primary task, there is currently no consensus of the effects of mind wandering on driving performance.

The purpose of this research was to investigate the frequency of mind wandering over repeated exposure to the same driving route, as well as to identify the relationship between mind wandering and both driver behavior and electrophysiology.

Data reduction was performed using MATLAB, with statistical analyses performed using R (R Core Team, 2015). Specifically, for measures of driver behavior and electrophysiology obtained during each drive, participant attentional state was labeled according to participant response following each attentional probe presentation. Further, the response to each probe was used to label the period of data that had occurred within the 10 s prior to the onset of that probe (see Figure 2).

For data from the driving simulator, measures of mean activity (e.g., speed) and variance (e.g., speed variability) were derived within each 10-s period. For data from the EEG, due to the potential of EEG artifact, each 10-s period was first split into 10, 1-s epochs, with any 1-s period containing artifact removed from further analysis and only the remaining 1-s periods averaged as representative of that 10-s period. As previously noted, attentional probes that occurred within or up to 10-s following a roadway curve were excluded from analysis. Finally, all data were winsorized to ±3 standard deviations from the mean of the corresponding outcome measure (Dixon and Tukey, 1968).

Driver behavior and physiology were analyzed using linear mixed effects modeling. The linear mixed effects analyses were performed using the Linear Mixed-Effects Models using “Eigen” and S4 (lme4) package (Bates et al., 2015) and the Tests in Linear Mixed Effects Models (lmerTest) package (Kuznetsova et al., 2016) for R. As SRR is a count variable (i.e., number of reversals per second), a poisson generalized linear mixed model was analyzed using lme4 and lmerTest. Because participants varied in the number of mind wandering instances reported, Satterthwaite type III approximations were used to calculate the denominator degrees of freedom. The fixed effects for all models included state (mind wandering, on task) and drive (drive one, drive two) as categorical variables, entered as sum contrasts (−1, 1) and day as a continuous variable, mean centered across the data set. Model intercepts were set as a random effect, allowing participants to have varying intercepts.

The experimental design was intended to roughly simulate drives to and from work, separated by a cognitively depleting work task. In this study, participants drove the same highway route twice a day for 5 days. Between the two drives on a given day, participants completed a task requiring sustained attention, the SART. Participants self-reported their current attentional state, indicating whether they were either “on-task” or “mind wandering”, in response to periodic probe tones. Additionally, following mind wandering responses, participants indicated whether they were aware or not aware of their mind wandering prior to the attentional probes.

The driving scenarios were designed to be rather repetitive and monotonous in order to increase the incidence of mind wandering (Berthié et al., 2015; Thomson et al., 2015) and ensure that enough instances of mind wandering would be generated for study. On average, 70.10% of the probes in the present study were responded to as “mind wandering” by the study participants. In contrast, Killingsworth and Gilbert (2010) report the results of a study probing individuals as to the content of their thoughts throughout everyday life outside the laboratory, reporting that participants respond that they are thinking about something other than what they are currently doing approximately 47% of the time. The high frequency of mind wandering in the present experiment would likely be lessened if the driving scenarios were made to be more demanding. For example, Lin et al. (2016) report a study measuring EEG activity in a motion-base driving simulator, asking participants to detect lane departures using either visual and motion information (the simulator could move in response to road conditions such as rumble strips), or only visual information (the simulator’s motion capabilities were deactivated). The authors report less DMN activity when participants were performing the more demanding version of the task without motion information. The addition of ambient traffic, or a more complex navigation task that also required navigation would also likely decrease the prevalence of self-reported mind wandering.

With consideration of previous work that has suggested increases in driver inattention as route familiarity increases (Yanko and Spalek, 2014), it was expected that mind wandering frequency might increase over the 5 days of participation. Instead, mind wandering frequency did not significantly differ over days of participation, though mind wandering did increase for the second drive relative to the first drive within the same day of participation. It is possible that the repetitive nature of the drives induced a maximum amount (or ceiling effect) of mind wandering while still being able to safely operate the simulated vehicle, thereby reducing the potential to see increased mind wandering frequency across days. The SART was performed between drives to roughly simulate performing a work task between commutes, and to ensure that enough mind wandering instances were available for study. As all participants performed the SART, we cannot comment on whether the increase in mind wandering frequency in the second drive of the present study is due to increased route familiarity, resource depletion from the SART, or a time on task effect.

Participants became significantly more aware that they were mind wandering across days. Specifically, for probe responses that were labeled mind wandering, there was a significant increase over days of participation in the frequency that participants reported that they were “aware” of their mind wandering at the time of the probe. It should be noted that the work of Yanko and Spalek (2014) did not query participant subjective state, and instead measured participant inattention by assessing their ability to detect hazards in the environment. The present experiment, in contrast, did ask participants to report their subjective attentional state, but did not include roadway hazards. Thus the present results may not contradict Yanko and Spalek (2013) but instead suggest a more complex relationship between driving performance and participant awareness of their attentional state. In the present experiment, when examining the 10-s periods preceding the attentional state probes, EEG alpha power increased over days of participation, in addition to more generally being elevated during periods of mind wandering. Taken together, it is possible that participants did become somewhat less attentive to the driving environment over days of participation in the present study, despite the lack of increase in subjective mind wandering reports over days of participation.

Power in the alpha band of the EEG was of greater magnitude during periods of self-reported mind wandering, relative to periods of self-reported on-task performance. Greater alpha power during periods of task inattention has also been reported for a variety of non-driving tasks. For example, O’Connell et al. (2009) report that within the continuous temporal expectancy task (CTET), the magnitude of alpha power over parietal electrodes is elevated prior to trials in which participants missed the target of interest, relative to trials in which participants correctly detected the target. Importantly, the increase in alpha band activity for miss trials was detectable up to 20 s prior to trial onset, suggesting that a slow modulation of top-down control contributes to lapses of attention within the CTET. Alpha power has further been related to inattention within a driving context more specifically. Within a driving simulator task in which participants were provided auditory notifications of lane departures, notifications that were behaviorally successful were associated with decreased alpha power following the notification, while for ineffective notifications alpha power remained elevated (Lin et al., 2013). Although consistent with other work on lapses of attention, an increase in alpha power during mind wandering is in contrast to what has been reported for self-detected mind wandering in a breath-counting task, wherein alpha power is suppressed immediately prior to self-detected mind wandering (Braboszcz and Delorme, 2011). This apparent difference may be resolved by considering that the alpha oscillation may serve a different role within the primary task used within the present study, simulated driving, in comparison to the primary task used within Braboszcz and Delorme (2011), eyes closed breath-counting. Future exploration of the possibility of using alpha power as a potential predictor of the mind wandering state, and thus a greater probability of missing a potentially hazardous event warrant further research. A supplementary method of examining a possible predictive potential for participants to miss critical events can be found in analysis of the ERPs.

Participants were periodically presented with an auditory tone, notifying them to indicate their current attentional state. Analysis of the ERP to this probe tone suggested that the P3a component was larger in response to tones that were subsequently responded to as “on task”, relative to tones that were subsequently responded to as “mind wandering”. As the P3a component is thought to reflect the orienting of attention towards a novel stimulus (Polich, 2007, 2011), this result supports the decoupling hypothesis of mind wandering (Smallwood, 2011) and is suggestive that participant attention towards the external environment was diminished during periods that were subjectively labeled as “mind wandering” relative to “on task”. Despite previous reports that mind wandering modulates the amplitude of early sensory components of the ERP (Baird et al., 2014; Broadway et al., 2015), we did not observe any statistically significant modulation of the auditory N1 component to the probe tones by attentional state. As early sensory components are known to be modulated by attention (Luck et al., 2000), the lack of an effect on the auditory N1 may reflect a lack of top-down attention towards the auditory stimulus in either the mind wandering or on task state. The auditory tone in the present experiment was supra-threshold, unpredictable with respect to onset, and did not require discrimination, only simple detection in order to provoke an attentional probe response. This is in contrast to influences of mind wandering on early sensory components in previous reports, for which the ERP is time-locked to a primary task stimulus (Baird et al., 2014; Broadway et al., 2015).

There are several limitations that should be noted. Our participants were restricted to young (18–29 years) individuals free from disease and other visual or health impairments that might compromise driving. It is not known whether mind wandering might manifest similarly in an older population or in individuals with certain health disorders. Further, none of our participants were shift workers and therefore it is unknown whether disruptions in circadian rhythms might impact performance or the underlying physiology. Additionally, the results were obtained in a driving simulator under carefully controlled conditions. More variability in both environment and behavior could be expected under naturalistic driving conditions.

In summary, both driving behavior and EEG activity demonstrated sensitivity off-line to distinguishing between periods of self-reported mind wandering vs. being on task. These results are largely in line with previous studies on mind wandering during driving, and on attentional processes as assessed with EEG, and support that mind wandering has an impact on both driving performance and the driver’s underlying physiology. Future work could extend these results by examining more closely a driver’s reaction to potential hazard situations when mind wandering vs. alert. Drivers may be expected to be less likely to react appropriately to a potential hazard (particularly if it occurs in peripheral vision since during mind wandering since gaze is narrowly focused more centrally). Further, the current results suggest drivers may be less likely to detect an auditory or visual warning while mind wandering. Future work should examine the potential for advanced auditory warnings to aid hazard mitigation in differing attentional states.

CLB, DMR, DB, JDL, NL and JSH contributed to the design of the experiment, DMR and DB collected and analyzed the data. CLB, DMR, DB, NL and JSH contributed to the writing of the manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.